BayesHammer: Bayesian clustering for error correction in single-cell sequencing - Nikolenko, Sergey I; Korobeynikov, Anton I; Alekseyev, Max A (BMC genomics, 2013)
Error correction of sequenced reads remains a difficult task, especially in single-cell sequencing projects with extremely non-uniform coverage. While existing error correction tools designed for standard (multi-cell) sequencing data usually come up short in single-cell sequencing projects, algorithms actually used for single-cell error correction have been so far very simplistic. We introduce several novel algorithms based on Hamming graphs and Bayesian subclustering in our new error correction tool B AYES H AMMER . While B AYES H AMMER was designed for single-cell sequencing, we demonstrate that it also improves on existing error correction tools for multi-cell sequencing data while working much faster on real-life datasets. We benchmark B AYES H AMMER on both k -mer counts and actual assembly results with the SPA DES genome assembler.